In this paper, we consider all spans up to 250 antecedents and 10 words.
In our BERT setup, we use the pre-trained BERT embeddings giving in [1]}. Both the actor and critic networks use the LSTM structure with hidden layer size of 200. The feedforward neural networks used to generate mention detection scores include two hidden layers with 150 units and ReLU activations. To be comparable with previous popular models, we include features (speaker ID, document genre, span distance and span width) as the 20-dimensional learned embeddings. For the \(f_{nn}^{(1,2,3)}\) , we use a dropout rate of 0.5.
<TABLE><TABLE>